About This Course

Why take this course?

This course will teach you how to use large language models to build generative AI applications. In addition to various models, it focuses on vector databases and the LangChain framework.

Artificial intelligence (AI) has witnessed unprecedented advancements in recent years, particularly in generative AI and large language models. Generative AI is like a digital artist who can create content independently. AI produces new and coherent things, whether writing paragraphs, generating images, or coming up with creative ideas. This ability makes it useful in tasks like writing assistance, chatbots, and content creation.

Large language models, like GPT-3.5, are significant milestones in scalable and contextually aware language understanding. These models are big and smart, especially when understanding and processing vast amounts of human language. They can comprehend context and nuances and even generate human-like text. This makes them incredibly useful in applications like virtual assistants, language translation, and helping computers better communicate with humans.

Target audience

This course is for software developers/engineers who may not be machine learning experts but want to leverage the latest advancements in AI/ML to build intelligent applications. It adopts a practical, hands-on approach with lots of sample applications. With generative AI and managed ML platforms, developers have many more options than just being restricted to using Python.

Prerequisites

Some programming experience with Go is preferable because we will use Go programs to build generative AI applications using large language models (LLMs). However, please note that the concepts apply to any other language we might choose (Java, for example).

Prior experience with machine learning is desirable but not mandatory to benefit from this course.

Course contents

• Introduction to fundamental concepts such as generative AI, large language models, and prompt engineering.

• Cover market-leading large language models and learn how to use them to build simple applications for text summarization, problem-solving, etc.

• Vector databases, their different types, and how they power semantic/vector search.

• Dive deep into two vector databases – PostgreSQL and Pinecone.

• LangChain framework and its components including LLMs, chains, etc.

• How to build practical applications using the LangChain framework.

• Extending the LangChain framework by integrating it with other components.

• Some of the practical demonstrations covered in this course include:

  • Building a simple movie recommendation app using vector databases,

  • Using the LangChain framework to build a generic chatbot,

  • Develop an application to ask questions about the contents of the documents and more.

• The course also has a project assignment to apply the skills we have learned.

Hope you enjoy the course!